With the rapid advance in industrial automation in recent years, inspection methods using machine vision are becoming more and more common, while simultaneously following the enhancement in process capability, the demand for higher detection precision is increasing. Conventionally the precision of detection is enhanced either by increasing the amount of image capturing devices in the detection area, or by replacing the current image capturing devices with other image capturing devices of higher pixel density. On the other hand, the detection can also be improved by scaling down the area of detection, by reducing visual range, or by adopting high zoom lenses for obtaining images with higher resolution. Nevertheless, although the detection precision is enhanced, sometimes it is achieved at the cost that inspection might not be able to proceed as the workpiece of interest or the interest area can not be captured completely inside the visual fields of the image capturing devices for detection.
In addition, conventionally an image composition process is performed based upon the acquisition of related image information. For example, for integrating two images into one composite image, the characteristic information of the two images is acquired first so as to identify the overlapping between the two images to be used in the image composition. Considering an inspection process for a specific food production line, the objective of the inspection process is to detect and identify problematic debris and scrap in a detection area that is generally filled will all kinds of pastes and stuffing. However, it is difficult to achieve as those pastes and stuffing are all about the same size and thus the difference between the resulting characteristic information extracted from detection images may not be obvious enough for forming a composite image with sufficient resolution.